Transformer-Based Model for Multilingual Hope Speech Detection
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866917238577037312 |
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| author | Ashraf, Nsrin Labib, Mariam Nayel, Hamada |
| author_facet | Ashraf, Nsrin Labib, Mariam Nayel, Hamada |
| contents | This paper describes a system that has been submitted to the "PolyHope-M" at RANLP2025. In this work various transformers have been implemented and evaluated for hope speech detection for English and Germany. RoBERTa has been implemented for English, while the multilingual model XLM-RoBERTa has been implemented for both English and German languages. The proposed system using RoBERTa reported a weighted f1-score of 0.818 and an accuracy of 81.8% for English. On the other hand, XLM-RoBERTa achieved a weighted f1-score of 0.786 and an accuracy of 78.5%. These results reflects the importance of improvement of pre-trained large language models and how these models enhancing the performance of different natural language processing tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_00613 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Transformer-Based Model for Multilingual Hope Speech Detection Ashraf, Nsrin Labib, Mariam Nayel, Hamada Computation and Language This paper describes a system that has been submitted to the "PolyHope-M" at RANLP2025. In this work various transformers have been implemented and evaluated for hope speech detection for English and Germany. RoBERTa has been implemented for English, while the multilingual model XLM-RoBERTa has been implemented for both English and German languages. The proposed system using RoBERTa reported a weighted f1-score of 0.818 and an accuracy of 81.8% for English. On the other hand, XLM-RoBERTa achieved a weighted f1-score of 0.786 and an accuracy of 78.5%. These results reflects the importance of improvement of pre-trained large language models and how these models enhancing the performance of different natural language processing tasks. |
| title | Transformer-Based Model for Multilingual Hope Speech Detection |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2602.00613 |